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Dear all, This is a list which may be of interest to the list! http://www.cam.ac.uk/research/news/twenty-top-tips-for-interpreting-scientific-claims The tips are (in brief): 1. Differences and chance cause variation. 2. No measurement is exact. 3. Bias is rife. 4. Bigger is usually better for sample size. 5. Correlation does not imply causation. 6. Regression to the mean can mislead. 7. Extrapolating beyond the data is risky. 8. Beware the base-rate fallacy. 9. Controls are important. 10. Randomization avoids bias. 11. Seek replication, not pseudoreplication. 12. Scientists are human. 13. Significance is significant. 14. Separate no effect from non-significance. 15. Effect size matters. 16. Study relevance limits generalisations. 17. Feelings influence risk perception. 18. Dependencies change the risks. 19. Data can be dredged or cherry picked. 20. Extreme measurements may mislead. Some overlap between them, natch. In any case, it's welcome to see these thorny issues being aired so candidly (and under a CC-BY-SA license, too!). Regards, Sam ********************************************************************** Commands - send an email (any subject) to [log in to unmask] with one of the following messages (ignoring text in brackets) • set psci-com nomail (to stop receiving messages while on holiday) • set psci-com mail (to resume getting messages) • signoff psci-com (to leave the list) • Subscribe here https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=psci-com Contact list owner at [log in to unmask] Small print and JISCMail acceptable use policy https://sites.google.com/site/pscicomjiscmail/the-small-print **********************************************************************